LeJEPA: SIGReg chunks cut VRAM use and runtime
A report published on March 4, 2026 says splitting 256 SIGReg slices into chunks of 32 used eight times less VRAM and finished faster on a 4090 MaxQ GPU.
Source: Dividir slices de SIGReg reduz o uso de VRAM no LeJEPA (x.com). Text prepared with AI from this source.
What happened and what to do
On March 4, 2026, a report on LeJEPA said that splitting 256 SIGReg slices into chunks of 32 used eight times less VRAM and finished faster on a 4090 MaxQ. It identified large complex-exponential tensors as the bottleneck.
A team training models on memory-limited GPUs could test this chunking approach and measure memory use and runtime on its own workload. The consultancy can help instrument tests, compare configurations, and integrate the optimization into the training pipeline, without assuming the same gains will apply in every environment.
How the consultancy can help
Wendelmaques can diagnose VRAM use and pipeline bottlenecks, scope an implementation for chunking tests, and support its operation and monitoring on the client's infrastructure.
Next step
Send a short description of your training workload, available GPUs, and memory limits to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.